Civil unrests can pose a severe threat to the security of individuals, communities, states and regions. Methods for accurate estimation of signs of higher than usual activity serve the purpose of providing security at different levels. Predicting crises and instabilities has been a research priority for many years.
A vast body of literature reflects the need for identification of tweet topics (see Literature Reference Nos. 9-14 in the List of Incorporated Literature References) which would be helpful in providing information about event types and groups of people who are instigating the civil unrest (e.g., protestors). The geolocations of Tweets can be determined using tweet tags (see Literature Reference No. 15) or user's social networks (see Literature Reference No. 16) that can accurately infer locations for nearly all of the individuals by spatially propagating location assignments through the social network using only a small number of initial locations.
Twitter™ is a convenient media to attempt to identify future protest events directly from public micro blogs and tweets/retweets. A number of scientists reached success in predicting social unrest using information contained in Twitter™ (see Literature Reference Nos. 11, 17-21). The method presented by researchers in Literature Reference No. 18 detects potential events by identifying keywords, so-called event-term candidates, whose frequency suddenly becomes significantly higher than expected. Then, all tweets containing the candidate terms are used to compute the scores capturing different characteristics of the event. In Literature Reference No. 21, the authors propose identification of informative posts by applying multiple textual and geographic filters to a high-volume data feed consisting of tens of millions of posts per day which have been flagged as public by their authors. Predictions are then built by annotating the filtered posts, typically a few dozen per day, with demographic, spatial, and temporal information.
Google™ Trends has also been used for predicting civil unrest events. For example, authors in Literature Reference No. 22 present a model which relies on search volumes of event-related terms and their momenta for prediction. The authors posit that an increased interest in civil unrest terms will produce observable real-time behavior consistent with those terms. However, existing methods for predicting and monitoring social events rely on available official statistics.
Thus, a continuing need exists for a system for monitoring and predicting social processes in social media before official statistics are available.